Abstract
This paper investigates certain fault analysis such as fault detection, identification and classification and their fault ride through (FRT) technique in a smart grid (SG) system using Stockwell transform (ST) and solid-state fault current limiter (SSFCL). ST when applied to symmetrical and asymmetrical faults for the detection and identification in a SG System yields a ST amplitude matrix (STA). The nature of the fault is identified through the features extracted from ST. STA with probabilistic neural network (PNN) classifier helps to detect the types of fault through the features extracted from fault signal. The outcome of PNN helps to classify the nature of fault like single-phase, two-phase and three-phase faults individually and with respect to ground fault in a SG system. Also, limiting the fault current ensures the continuous operation and reliability of SG under fault conditions. Further to avoid the disconnection of wind turbine system and solar PV system from the grid and overcome block out issue, SSFCL is employed. It improves the FRT capability of a SG system by controlling the fault current within the specified limit and retains the wind turbine system and solar PV system connected with the grid. The suggested scheme is modeled and the results are verified through the time domain simulation using MATLAB.
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Abbreviations
- CWT:
-
Continuous wavelet transform
- DFIG:
-
Doubly fed induction generator
- DVR:
-
Dynamic voltage restorer
- EMD:
-
Empirical mode decomposition
- FFT:
-
Fast Fourier transform
- FCL:
-
Fault current limiting
- FCLID:
-
Fault current limiting and interrupting devices
- FRT:
-
Fault ride through
- FT:
-
Fourier transform
- HHT:
-
Hilbert–Huang transform
- HAS:
-
Hilbert spectral analysis
- IMF:
-
Intrinsic mode functions
- LVRT:
-
Low-voltage ride through
- PNN:
-
Probabilistic neural network
- STFT:
-
Short time Fourier transform
- SG:
-
Smart grid
- SSFCL:
-
Solid-state fault current limiter
- SSCB:
-
Solid-state circuit breaker
- STA:
-
Stockwell transform amplitude matrix
- ST:
-
Stockwell transform
- SCFCL:
-
Superconducting fault current limiter
- TFR:
-
Time–frequency representation
- WT:
-
Wavelet transform
- WECS:
-
Wind energy conversion system
References
Hare J, Shi X, Gupta S, Bazzi A (2016) Fault diagnostics in smart micro-grids: a survey. Renew Sustain Energy Rev 60:1114–1124
Chen K, Huang C, He J (2016) Fault detection, classification and location for transmission lines and distribution systems: a review on the methods. High Volt 1:25–33
Gush T, Bukhari SBA, Haider R, Admasie S, Oh Y-S, Cho G-J, Kim C-H (2018) Fault detection and location in a microgrid using mathematical morphology and recursive least square methods. Int J Electr Power Energy Syst 102:324–331
Oh Y-S, Kim C-H, Gwon G-H, Noh C-H, Bukhari SBA, Haider R, Gush T (2019) Fault detection scheme based on mathematical morphology in last mile radial low voltage DC distribution networks. Int J Electr Power Energy Syst 106:520–527
Hooshyar A, El-Saadany EF, Sanaye-Pasand M (2016) Fault type classification in microgrids including photovoltaic DGs. IEEE Trans Smart Grid 7:2218–2229
Salim RH, Salim KCO, Bretas AS (2011) Further improvements on impedance-based fault location for power distribution systems. IET GenerTransmDistrib 5:467
Aftab MA, Hussain SMS, Ali I, Ustun TS (2020) Dynamic protection of power systems with high penetration of renewables: a review of the traveling wave based fault location techniques. Int J Electr Power Energy Syst 114:105410
Mishra M, Rout PK (2018) Detection and classification of micro-grid faults based on HHT and machine learning techniques. TransmDistrib IET Gener 12:388–397
Yu JJQ, Hou Y, Lam AYS, Li VOK (2019) Intelligent fault detection scheme for microgrids with wavelet-based deep neural networks. IEEE Trans Smart Grid 10:1694–1703
Abdelgayed TS, Morsi WG, Sidhu TS (2018) A new approach for fault classification in microgrids using optimal wavelet functions matching pursuit. IEEE Trans Smart Grid 9:4838–4846
Abdelgayed TS, Morsi WG, Sidhu TS (2018) Fault detection and classification based on co-training of semisupervised machine learning. IEEE Trans Ind Electron 65:1595–1605
Hong Y-Y, Cabatac MTAM (2019) Fault detection, classification, and location by static switch in microgrids using wavelet transform and Taguchi-based artificial neural network. IEEE Syst J 14:2725–2735
Stockwell RG, Mansinha L, Lowe RP (1996) Localization of the complex spectrum: the S transform. IEEE Trans Signal Process 44:998–1001
Mishra PK, Yadav A, Pazoki M (2019) FDOST-based fault classification scheme for fixed series compensated transmission system. IEEE Syst J 13:3316–3325
Chen YQ, Fink O, Sansavini G (2018) Combined fault location and classification for power transmission lines fault diagnosis with integrated feature extraction. IEEE Trans Ind Electron 65:561–569
Saber A (2018) A new fault location technique for four-circuit series-compensated transmission lines. Int Trans Electr Energy Syst. https://doi.org/10.1002/etep.2791
Ashok V, Yadav A (2019) A real-time fault detection and classification algorithm for transmission line faults based on MODWT during power swing. Int Trans Electr Energy Syst. https://doi.org/10.1002/2050-7038.12164
Roy N, Bhattacharya K (2016) Signal analysis-based fault classification and estimation of fault location of an unbalanced network using S-transform and neural network. Int Trans Electr Energy Syst. https://doi.org/10.1002/tee.22256
Kumar PKA, Vijayalakshmi VJ, Karpagam J, Hemapriya CK (2016) Classification of power quality events using support vector machine and S-transform. In: 2nd International conference on contemporary computing and informatics (IC3I), Noida, 2016, pp 279–284. https://doi.org/10.1109/IC3I.2016.7917975
Kubendran AKP, Loganathan AK (2017) Detection and classification of complex power quality disturbances using S-transform amplitude matrix-based decision tree for different noise levels. Int Trans Electr Energy Syst 27(4):1–12
Lala H, Karmakar S, Ganguly S (2018) Detection and localization of faults in smart hybrid distributed generation systems: a Stockwell transform and artificial neural network-based approach. Int Trans ElectrEnergSyst. https://doi.org/10.1002/etep.2725
Puliyadi Kubendran AK, Ashok Kumar L (2020) Detection and classification of power quality events using wavelet energy difference and support vector machine. In: Kumar L, Jayashree L, Manimegalai R (eds) Proceedings of international conference on artificial intelligence, smart grid and smart city applications. AISGSC 2019, 2020. Springer, Cham. https://doi.org/10.1007/978-3-030-24051-6_3
Dharmapandit O, Patnaik RK, Dash PK (2017) Detection, classification, and location of faults on grid-connected and islanded AC microgrid. Int Trans Electr Energy Syst. https://doi.org/10.1002/etep.2431
Hemapriya CK, Suganyadevi MV, Krishnakumar C (2020) Detection and classification of multi-complex power quality events in a smart grid using Hilbert–Huang transform and support vector machine. ElectrEng. https://doi.org/10.1007/s00202-020-00987-8
Tsili M, Papathanassiou S (2009) A review of grid code technical requirements for wind farms. IET Renew Power Gener 3:308–332
Justo JJ, Mwasilu F, Jung J-W (2015) Doubly-fed induction generator based wind turbines: a comprehensive review of fault ride-through strategies. Renew Sustain Energy Rev 24:447–467
Huchel L, Moursi MSE, Zeineldin HH (2015) A parallel capacitor control strategy for enhanced FRT capability of DFIG. IEEE Trans Sustain Energy 6:303–312
Arunkumar PK, Kannan SM, Selvalakshmi I (2016) Low voltage ride through capability improvement in a grid connected wind energy conversion system using STATCOM. In: International conference on energy efficient technologies for sustainability (ICEETS), Nagercoil, 2016, pp 603–608
Kumar PKA, Vivekanandan S, Kumar CK, and Chinnaiyan VK (2016) Neural network tuned fuzzy logic power system stabilizer design for SMIB. In: 2nd International conference on contemporary computing and informatics (IC3I), Noida, 2016, pp 446–451. https://doi.org/10.1109/IC3I.2016.7918006
Kumar PKA, Uthirasamy R, Saravanan G, Ibrahim AM (2016) AGC performance enhancement using ANN. In: 2nd International conference on contemporary computing and informatics (IC3I), Noida, 2016, pp 452–456. https://doi.org/10.1109/IC3I.2016.7918007
Naderi SB, Negnevitsky M, Jalilian A, TarafdarHagh M, Muttaqi KM (2017) Low voltage ride-through enhancement of DFIG-based wind turbine using DC link switchable resistive type fault current limiter. Int J Electr Power Energy Syst 86:104–119
AngalaParameswari G, Habeebullah Sati H (2020) A comprehensive review of fault ride-through capability of wind turbines with grid-connected doubly fed induction generator. Int Trans Electr Energy Syst. https://doi.org/10.1002/2050-7038.12395
Guo W, Xiao L, Dai S (2016) Fault current limiter-battery energy storage system for the doubly-fed induction generator: analysis and experimental verification. IET Gener Trans Distrib 10:653–660
Zou ZC, Xiao XY, Liu YF, Zhang Y, Wang YH (2016) Integrated protection of DFIG-based wind turbine with a resistive-type SFCL under symmetrical and asymmetrical faults. IEEE Trans ApplSupercond 26:1–5
Puliyadi Kubendran AK, Ashok Kumar L (2020) LVRT Capability improvement in a grid-connected DFIG wind turbine system using neural network-based dynamic voltage restorer. In: Kumar L, Jayashree L, Manimegalai R (eds) Proceedings of international conference on artificial intelligence, smart grid and smart city applications. AISGSC 2019, 2020. Springer, Cham. https://doi.org/10.1007/978-3-030-24051-6_2
Puliyadi Kubendran AK, Ashok Kumar L, Cheren SE (2020) Performance comparison of power quality improvement strategies for unified power quality conditioner in an interconnected distribution system. In: Joint international conference on digital arts, media and technology with ECTI northern section conference on electrical, electronics, computer and telecommunications engineering (ECTI DAMT and NCON), Pattaya, Thailand, 2020, pp 180–185. https://doi.org/10.1109/ECTIDAMTNCON48261.2020.9090761
Sumathi S, Kumar LA, Surekha P (2015) Solar PV and wind energy conversion systems: an introduction to theory, modeling with MATLAB/SIMULINK, and the role of soft computing techniques. Springer, Berlin
Ashok Kumar L, Albert Alexander S (2018) Computational paradigm techniques for enhancing electric power quality. CRC Press, Boca Raton
Sumathi S, Kumar LA, Surekha P (2018) Computational intelligence paradigms for optimization problems using MATLAB/SIMULINK. CRC Press, Boca Raton
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Thinakaran, T., Loganathan, A.K. Certain investigations on fault analysis in a smart grid system using S-transform and their fault ride through using solid-state fault current limiter. Electr Eng 103, 2127–2145 (2021). https://doi.org/10.1007/s00202-021-01222-8
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DOI: https://doi.org/10.1007/s00202-021-01222-8